Now everyone around is talking about the benefits of big data.

As a result, businesses are trying to work with them, but often face a problem when all the data is heterogeneous and unstructured.

This means that such data needs to be processed for a long time before loading into databases.

So, working with big data turns out to be too complex and expensive, and some of the data is lost, although it could bring considerable benefit, both now and in future projects.

Collecting big data, loading it into data lakes and then processing it using machine learning tools is a science, especially when it comes to blockchain data.

Increasingly, startups, companies and even corporations are seeking to implement artificial intelligence and machine learning projects based on the blockchain infrastructure.

It is difficult to imagine solving such problems without using the tools of etl as a service.

About machine learning

It is obvious that in order for any business to successfully develop, create and implement new projects, it needs to constantly analyze various data, compare them, looking at them from different angles, and make forecasts for the future.

Machine learning (ML) plays a significant role in this activity.

Machine learning is a kind of offshoot of artificial intelligence. Its essence is not to oblige the computer to carry out a pre-written algorithm of actions for it, but to use self-learning technology to complete its assigned task.

There are three levels of availability of machine learning technology:

1 – ML is only available to tech giants like Google.

2 – ML can be used by a user with a certain basic level of knowledge.

3 – ML can be used freely by anyone.

In relation to today, ML occupies a position between the 2nd and 3rd levels, which contributes to rapid changes in the IT world.

If we continue the gradation of machine learning, we can see that there are the following types of it: supervised learning, unsupervised learning and reinforcement learning.

Supervised learning

The essence of this type of ML is that a set of certain training data is specified, on the basis of which the ML model remembers the structure of the task.

The data is generated by specially trained people – assessors. For example, the ML model is given the pictures of hummingbird and a lion with target labels – “hummingbird” and “lion”.

Target labels characterize the classes of data that are present in the problem. The tasks that can be solved using supervised learning are the following:

1 – Classification, in which the model builds a dividing line between categories. In our example, the model divides all data into two categories: the categoria of hummingbirds and the categoria of lions. The target values in this case are categorical or discrete. Here is another example of classification – the task of finding fraudulent transactions. It has a binary classification or two categories of data – fraudulent transactions and standard transactions.

2 – Regression, where the model draws a line that illustrates the law which our data follows. The target values are continuous or hylic. An example is the problem of predicting the value of real estate. Another example is predicting sales or demand for goods. This task is relevant when it is necessary to stabilize the supply of goods, especially if these are perishable products.

Unsupervised learning

The essence of this type of ML is to find certain patterns inside the data.

In this case, the model independently tries to understand the essence of the problem without prompting from the assessor.

There are no target labels, which allows us to better understand the structure of our data and solve two problems – clustering and dimensionality reduction.

1 – Clustering. Let’s return to the example with pictures of the hummingbird and the lion, but without the target labels “hummingbird” and “lion”. So, there are many pictures depicting various hummingbirds and lions, they need to be sorted. In this case, good images preprocessing is required, and then training the model a clustering on data segments. After this, the model will be able to produce groups of similar images, one of these groups will contain images of hummingbirds, and the other will contain images of lions. However, the names of these groups will not be “class of hummingbirds” and “class of lions”, but “cluster 1” and “cluster 2”. Since there are no target labels, the model collects clusters based on the similarity of image segments.

2 – Dimensionality reduction. Data for model training is most often multidimensional. However, a person cannot perceive multidimensional spaces, which means he will not be able to analyze such data. To prevent this from happening, there is a way to compress data by reducing its dimensionality.

Reinforcement learning

This type of training is based on the approach of positive and negative reinforcement.

That is, during the learning process, an agent interacts with the environment and receives rewards for “good actions” and punishments for “bad actions.”

In this case, “good actions” are those that bring the agent closer to the final goal, and “bad actions” are those that do not bring him closer to it.

The agent does not know what the end goal is, since it is only in the developer’s head.

The agent’s task is to determine the correct actions to get as many rewards as possible.